Provides a collection of TFLite model analyzer tools.
Used in the notebooks
Used in the guide
Example:
model
=
tf
.
keras
.
applications
.
MobileNetV3Large
()
fb_model
=
tf
.
lite
.
TFLiteConverterV2
.
from_keras_model
(
model
)
.
convert
()
tf
.
lite
.
experimental
.
Analyzer
.
analyze
(
model_content
=
fb_model
)
# === TFLite ModelAnalyzer ===
#
# Your TFLite model has ‘1’ subgraph(s). In the subgraph description below,
# T# represents the Tensor numbers. For example, in Subgraph#0, the MUL op
# takes tensor #0 and tensor #19 as input and produces tensor #136 as output.
#
# Subgraph#0 main(T#0) -> [T#263]
# Op#0 MUL(T#0, T#19) -> [T#136]
# Op#1 ADD(T#136, T#18) -> [T#137]
# Op#2 CONV_2D(T#137, T#44, T#93) -> [T#138]
# Op#3 HARD_SWISH(T#138) -> [T#139]
# Op#4 DEPTHWISE_CONV_2D(T#139, T#94, T#24) -> [T#140]
# ...
Methods
analyze
@staticmethodanalyze ( model_path = None , model_content = None , gpu_compatibility = False , ** kwargs )
Analyzes the given tflite_model with dumping model structure.
This tool provides a way to understand users' TFLite flatbuffer model by dumping internal graph structure. It also provides additional features like checking GPU delegate compatibility.
Args
model_path
TFLite flatbuffer model path.
model_content
TFLite flatbuffer model object.
gpu_compatibility
Whether to check GPU delegate compatibility.
**kwargs
Experimental keyword arguments to analyze API.
Returns
Print analyzed report via console output.
